Zusammenfassung
Genetic programming (GP) extends traditional genetic
algorithms to automatically induce computer programs.
GP has been applied in a wide range of applications
such as software re-engineering, electrical circuits
synthesis, knowledge engineering, and data mining. One
of the most important and challenging research areas in
GP is the investigation of ways to successfully evolve
recursive programs. A recursive program is one that
calls itself either directly or indirectly through
other programs. Because recursions lead to compact and
general programs and provide a mechanism for reusing
program code, they facilitate GP to solve larger and
more complicated problems. Nevertheless, it is commonly
agreed that the recursive program learning problem is
very difficult for GP. In this paper, we propose
techniques to tackle the difficulties in learning
recursive programs. The techniques are incorporated
into an adaptive Grammar Based Genetic Programming
system (adaptive GBGP). A number of experiments have
been performed to demonstrate that the system improves
the effectiveness and efficiency in evolving recursive
programs.
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